A Value Classification of Electronic Product Reviews Based on Maximum Entropy

Reviews of electronic product are important resources for manufacturers to gather feedbacks from customers. They provide an important reference for customers to make a decision on product purchase. Electronic product reviews with high quality is valuable, and reviews analysis is becoming an open issue. Based on the binary feature function, a maximum entropy classification of electronic product reviews is proposed to identify the valuable reviews. Considering the simpleness of the theme in reviews, a text classifier integrated with the features of descriptive anaphor is provided to enhance the model's fitting degree. Furthermore, the feature library is constructed by the Term frequency-inverse document frequency (TF-IDF) algorithm to locate the description, and the descriptive anaphors are identified by means of the minimum Inflectional phrase (IP) parsing tree. The experiment shows the proposed classifier has the relatively high stability and precision, which makes the reviews much more usable for manufacturers to make product strategy and for customers to make a decision.